Quality Control of Expression Profiling Data. / Soloviev, Mikhail; Milnthorpe, Andrew.

In: Journal of Proteomics & Bioinformatics, Vol. 8, 7, 22.07.2015, p. 176-187.

Research output: Contribution to journalArticlepeer-review

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Quality Control of Expression Profiling Data. / Soloviev, Mikhail; Milnthorpe, Andrew.

In: Journal of Proteomics & Bioinformatics, Vol. 8, 7, 22.07.2015, p. 176-187.

Research output: Contribution to journalArticlepeer-review

Harvard

Soloviev, M & Milnthorpe, A 2015, 'Quality Control of Expression Profiling Data', Journal of Proteomics & Bioinformatics, vol. 8, 7, pp. 176-187. https://doi.org/10.4172/jpb.1000366

APA

Vancouver

Soloviev M, Milnthorpe A. Quality Control of Expression Profiling Data. Journal of Proteomics & Bioinformatics. 2015 Jul 22;8:176-187. 7. https://doi.org/10.4172/jpb.1000366

Author

Soloviev, Mikhail ; Milnthorpe, Andrew. / Quality Control of Expression Profiling Data. In: Journal of Proteomics & Bioinformatics. 2015 ; Vol. 8. pp. 176-187.

BibTeX

@article{c2ee953ddbcc48afaeb4c58df7d663ee,
title = "Quality Control of Expression Profiling Data",
abstract = "Expression profiling is a popular tool for studying gene expression levels, but libraries{\textquoteright} origins and data quality are often poorly annotated or contain errors. Experimental techniques, library annotations and analysis algorithms vary between laboratories and may contain errors. Traditional analysis methods, including research into tissuespecific expression, assume expression levels to be correct and libraries to be correctly annotated, which is not always the case. Therefore, tools capable of assessing the quality of multiple types of expression data using the data alone would be invaluable for quality control of that data and elucidation of its suitability for expression analysis. Here we compare and review over 20 methods and focus on a number of key developments in the field. We also highlight the application of recently devised novel quality control methods and show examples of applications of the newly developed quality control expression matrixes (QCEM) to the analysis and quality control of SAGE data. The described example include elucidating the correct tissue identity and show that disease state for expression libraries created using a range of expression profiling methods might be easily elucidated. The described novel quality control methods address key shortcomings of the previously reported tools and provide a universal quality control method for multiple types of expression data.",
author = "Mikhail Soloviev and Andrew Milnthorpe",
year = "2015",
month = jul,
day = "22",
doi = "10.4172/jpb.1000366",
language = "English",
volume = "8",
pages = "176--187",
journal = "Journal of Proteomics & Bioinformatics",

}

RIS

TY - JOUR

T1 - Quality Control of Expression Profiling Data

AU - Soloviev, Mikhail

AU - Milnthorpe, Andrew

PY - 2015/7/22

Y1 - 2015/7/22

N2 - Expression profiling is a popular tool for studying gene expression levels, but libraries’ origins and data quality are often poorly annotated or contain errors. Experimental techniques, library annotations and analysis algorithms vary between laboratories and may contain errors. Traditional analysis methods, including research into tissuespecific expression, assume expression levels to be correct and libraries to be correctly annotated, which is not always the case. Therefore, tools capable of assessing the quality of multiple types of expression data using the data alone would be invaluable for quality control of that data and elucidation of its suitability for expression analysis. Here we compare and review over 20 methods and focus on a number of key developments in the field. We also highlight the application of recently devised novel quality control methods and show examples of applications of the newly developed quality control expression matrixes (QCEM) to the analysis and quality control of SAGE data. The described example include elucidating the correct tissue identity and show that disease state for expression libraries created using a range of expression profiling methods might be easily elucidated. The described novel quality control methods address key shortcomings of the previously reported tools and provide a universal quality control method for multiple types of expression data.

AB - Expression profiling is a popular tool for studying gene expression levels, but libraries’ origins and data quality are often poorly annotated or contain errors. Experimental techniques, library annotations and analysis algorithms vary between laboratories and may contain errors. Traditional analysis methods, including research into tissuespecific expression, assume expression levels to be correct and libraries to be correctly annotated, which is not always the case. Therefore, tools capable of assessing the quality of multiple types of expression data using the data alone would be invaluable for quality control of that data and elucidation of its suitability for expression analysis. Here we compare and review over 20 methods and focus on a number of key developments in the field. We also highlight the application of recently devised novel quality control methods and show examples of applications of the newly developed quality control expression matrixes (QCEM) to the analysis and quality control of SAGE data. The described example include elucidating the correct tissue identity and show that disease state for expression libraries created using a range of expression profiling methods might be easily elucidated. The described novel quality control methods address key shortcomings of the previously reported tools and provide a universal quality control method for multiple types of expression data.

U2 - 10.4172/jpb.1000366

DO - 10.4172/jpb.1000366

M3 - Article

VL - 8

SP - 176

EP - 187

JO - Journal of Proteomics & Bioinformatics

JF - Journal of Proteomics & Bioinformatics

M1 - 7

ER -